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Determining Effective Swarm Sizes for Multi-Job Type Missions

Chandarana, Meghan and Lewis, Michael and Sycara, Katia and Scherer, Sebastian (2018) Determining Effective Swarm Sizes for Multi-Job Type Missions. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1-5 Oct 2018, Madrid, Spain.

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Swarm search and service (SSS) missions require large swarms to simultaneously search an area while servicing jobs as they are encountered. Jobs must be immediately serviced and can be one of several different job types - each requiring a different service time and number of vehicles to complete its service successfully. After jobs are serviced, vehicles are returned to the swarm and become available for reallocation. As part of SSS mission planning, human operators must determine the number of vehicles needed to achieve this balance. The complexities associated with balancing vehicle allocation to multiple as yet unknown tasks with returning vehicles makes this extremely difficult for humans. Previous work assumes that all system jobs are known ahead of time or that vehicles move independently of each other in a multi-agent framework. We present a dynamic vehicle routing (DVR) framework whose policies optimally allocate vehicles as jobs arrive. By incorporating time constraints into the DVR framework, an M/M/k/k queuing model can be used to evaluate overall steady state system performance for a given swarm size. Using these estimates, operators can rapidly compare system performance across different configurations, leading to more effective choices for swarm size. A sensitivity analysis is performed and its results are compared with the model, illustrating the appropriateness of our method to problems of plausible scale and complexity.


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Item Type: Conference or Workshop Item (Paper)
Status: Published
CreatorsEmailPitt UsernameORCID
Chandarana, Meghan
Lewis, Michaelml@sis.pitt.educmlewis0000-0002-1013-9482
Sycara, Katia
Scherer, Sebastian
Date: September 2018
Date Type: Publication
Journal or Publication Title: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher: IEEE
Event Title: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems
Event Dates: 1-5 Oct 2018
Event Type: Conference
DOI or Unique Handle: 10.1109/iros.2018.8593919
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
ISSN: 2153-0866
Article Type: Research Article
Date Deposited: 31 May 2019 17:43
Last Modified: 31 May 2019 17:43


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